1. Technical Field
The present invention relates generally to mobile networks and specifically to a method and apparatus for a real-time, transparent, network-based approach for capturing multi-dimensional user-level usage information on usage of content and providing correlated real-time reports.
2. Discussion of Related Art
Traditionally, mobile operators have had very tight control on the content that was being accessed on their networks and used to limit user access to a ‘walled garden’ or ‘on-deck content’. This was done for two reasons: to optimize their network for well-understood content, and to control user experience. With the advent of more open devices and faster networks, there is an increasing trend in the mobile community to access ‘off-deck’ or ‘off-portal’ content, which is content generally available on the Internet at large and not pre-selected content hosted by the operator. This movement is generally troubling to service providers for two reasons. First, service providers have very limited visibility in the usage of off-deck content and hence they don't have the ability to design and optimize their networks for this usage. Further, they also no longer have the ability to control what their users access and hence they fear becoming ‘dumb pipes’ and not participating in the whole movement towards advertising and monetizing Internet content.
Content providers on the other hand, are interested in the potentially huge increased customer base of mobile users. Further, the mobile device is highly personal and by getting specific information about users such as location, demographics, usage patterns, etc. they can generate very targeted content and advertising. However, they too lack detailed visibility about mobile users or about what is happening in the mobile network. While a client on the mobile handset could provide some of this, they can't put clients or other applications into mobile devices easily to get additional data since these devices are still fairly rudimentary in comparison with a PC. Also, due to the traditional lock-in on the devices form a mobile operator, the client on the device may not provide all the detailed information. For instance, user location is not easily exposed by carriers since they are concerned about privacy and its usage and also since its such a critical part of the carrier data. Recent developments such as the Android open platform from Google are attempts to open up some of this information. However, it is still up to the carriers to allow these devices on their networks and for device manufacturers to use this platform. Further, this restricts the ability of data collection only to the new devices that embrace this platform—a carriers network will continue to have many other devices as well.
A key requirement to enable these two silos—mobile carriers and content providers—to jointly evolve the mobile content ecosystem is to mine and share mobile content usage effectively. By getting visibility into off-deck mobile content usage, mobile operators can optimize their networks. Mobile carriers are sitting on a goldmine of data that includes user's location, access patterns, demographic information, etc. By systematically sharing information between mobile operators and content providers, it is possible to offer very targeted and relevant content to the users.
Existing methods do not provide a method to capture user information transparently across multiple dimensions in real-time. The existing approaches can be characterized by several categories of solutions.
The first approach used by network based Deep Packet Inspection vendors (e.g. Ellacoya) is to capture information only around a couple of dimensions, including application and bandwidth. For example, it helps answer questions such as—what fraction of users are running what application, or what fraction of bandwidth is used by what application. Also, these approaches don't allow for storage and analytics on the data.
The second approach used by event monitoring products such as Vallent put probes in the network to capture events generated by other network elements—they do not actually capture live user-level session data or do any correlation.
A third approach by instrumentation companies like Keynote focuses on capturing on-demand measurements through their own devices emulating real-world testing. Their focus is on how applications run on different handsets, on different networks. They allow content providers and carriers to test mobile applications on 1000 s of device profiles to make sure the application runs as expected. This is accomplished through virtual handsets deployed on the network. They also carry actual devices that can be to test applications. This “sampling” approach does not give specific user-level information that can be used for delivering a ‘relevant’ mobile experience.
A fourth approach by vendors such as Telephia/Nielson and Comscore/M:Metrics focuses on generated syndicated consumer research and panel based solutios. Their approach involves placing distributed monitors and collecting aggregated data for marketing and consumer usage characterization—they do no capture per-user data. Another approach is to place agents on the device to gather information. In either case, they don't collect from the network level and is hence restricted to collecting only representative data from field monitors.
A fifth approach used by traditional Web Analytics vendors (e.g. Omniture) relates to using logs on the application (e.g. HTTP). The traditional web approach does not work well for mobile applications for a number of reasons. First, this is restricted to a single application, which is HTTP. Mobile analytics requires a view across applications such as SMS, WAP, Downloads, etc. Further, these applications don't necessarily have logs and also logs tend to be time-delayed. Also, the web analytics tend to rely on client side support such as javascript, cookies, etc. which are not expected to be available universally on mobile devices. Unlike Web techniques, metrics such as unique user identifiers and location can't be derived for mobile devices from cookies or IP addresses. Cookies are not supported on mobile devices and IP addresses often tend to be masked when leaving the mobile network. Also, traditional web approaches to determine location through reverse IP mapping or other techniques don't apply since IP addresses are masked.